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import spacy

nlp = spacy.load("en_core_web_sm")
doc = nlp("Natural Language Processing is fascinating.")
tokens = [token.text for token in doc]
print(tokens)
# Output: ['Natural', 'Language', 'Processing', 'is', 'fascinating', '.']

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# using nltk

from nltk.stem import PorterStemmer

stemmer = PorterStemmer()
print(stemmer.stem("running"))  # Output: 'run'
print(stemmer.stem("flies"))    # Output: 'fli'

import spacy

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import numpy as np
import pandas as pd
import time

np.random.seed(2)  # reproducible

ROWS, COLS = 2, 2  # dimensions of the 2D world
ACTIONS = ['left', 'right', 'up', 'down']  # available actions
EPSILON = 0.9  # greedy policy
ALPHA = 0.1  # learning rate

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from spacy.lang.en.stop_words import STOP_WORDS

sentence = "This is a sample sentence."
doc = nlp(sentence)
filtered_tokens = [token.text for token in doc if token.text.lower() not in STOP_WORDS]

print(filtered_tokens)
# Output: ['sample', 'sentence', '.']

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from nltk.util import ngrams
from nltk.tokenize import word_tokenize

sentence = "I love coding and learning"
tokenized_sentence = word_tokenize(sentence)
bigrams = list(ngrams(tokenized_sentence, 2))
print(bigrams)
# Output: [('I', 'love'), ('love', 'coding'), ('coding', 'and'), ('and', 'learning')]

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import numpy as np
import pandas as pd
import time


class QLearningAgent:
    def __init__(self, n_states, actions, epsilon, alpha, gamma, max_episodes, fresh_time):
        self.n_states = n_states
        self.actions = actions
        self.epsilon = epsilon

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from langchain_experimental.data_anonymizer import PresidioAnonymizer
from faker import Faker

fake = Faker()

DATA = []

DATA_ANON = []

for _ in range(2):

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import re
import spacy
import hashlib
from faker import Faker

fake = Faker()
nlp = spacy.load("en_core_web_sm")

DATA = []
DATA_ANON = []

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from anonLLM.llm import OpenaiLanguageModel
from dotenv import load_dotenv

load_dotenv()

text = "Write a CV for me: My name is Alice Johnson, "\
    "email: [email protected], phone: +1 234-567-8910."\
    "I am a machine learning engineer."

llm = OpenaiLanguageModel()

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import tkinter as tk
import pandas as pd
import numpy as np
import time

class QLearningTable:
    # Initialize parameters and create a Q-table
    def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9):
        self.actions = actions
        self.lr = learning_rate